An Interpretable Machine Learning Model for Heart Arrhythmia Classification

Abstract

Machine learning (ML) has been a very effective tool for arrhythmia detection and classification using electrocardiograms (ECGs). However, in order for patients and healthcare professionals to trust the ML models, the models have to be interpretable to show how they arrived at a certain conclusion. We present an ML model that uses the Deep k-Nearest Neighbors framework in order to produce example-based explanations and uncertainty estimations. These examples are ECGs similar to the input derived from conducting a nearest neighbor search on encoded samples (which are values of a layer of the neural network after passing dataset samples through it). We introduce a new technique of using these example-based explanations in conjunction with saliency maps, and also use a neighbor-based uncertainty estimation technique. We show that the saliency maps provide good explanations, but the neighbor examples are needed to assess the credibility of those saliency maps. Our uncertainty estimations increase the accuracy of the model from 86% to 93% (when measured with coverage of 76%). Overall, our novel methods prove to be a promising solution in the field of interpretable ML for arrhythmia classification.